Automated assessments of key MQ Buffer Manager metrics facilitate proactive analysis and prevention of outages by identifying areas that may warrant investigation. These views also expedite learning by highlighting metrics that are important to understand and analyze.


More MQ Statistics Videos

  1. Overview of MQ Statistics and Health Assessments
  2. Assessing MQ Buffer Manager Health
  3. Assessing MQ Log Manager Health
  4. MQ Message Manager Metrics Supplemented by Accounting Data
  5. Log Manager and Buffer Manager Metrics
  6. Sample MQ Statistics and Accounting Dashboards


Video Transcript

So, we start out with a buffer pool health assessment here. As is the case with Db2, responsive performance from MQ relies on having your data, your messages residing in memory. So buffer pool management is an important aspect of MQ performance. And so that makes it a good area to have automatic assessments of every queue manager and every buffer pool to identify areas that might warrant investigation. MQ has a deferred right process that’s activated when a buffer pool reaches 85% full. And when that condition is reached, then that deferred right process will start to write the oldest data from the buffer pool out to disk to free up buffer pool pages for new messages that are arriving, and that continues until the utilization gets below 75%. So that destaging is particularly undesirable for short-lived messages. And then just note also messages that have remained in a buffer pool for three checkpoint intervals will also be written out to disk no matter what the buffer pool utilization is.

So in this example, we see exceptions, this is the high volume queue manager here. We see exceptions for a couple of different metrics. And so, initially let’s look and see which buffer pools it is. And it’s a couple of different buffer pools. So let’s go ahead now and, and view these metrics over time at a glance.

And we can see the first three metrics all have a kind of a spike going on somewhere in the 8 o’clock timeframe. So let’s start here with how many times we hit the deferred write threshold. And of course, that’s a lot of times to hit that threshold. So let’s compare that. And we talked about how that’s triggered by buffer utilization hitting 85%. So let’s go ahead and compare those on the same chart. And we can see, as expected, a very high degree of correlation. In fact, so high that the lines are almost on top of each other, right? You can hardly even see them. And if we want, we can go ahead and also compare to the rate of get page requests and we’ll see there also a high degree of correlation.

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